Reliability-Driven Quality Assurance in New Energy Industrial Equipment Supply Chains Under Complex Operating Conditions

Abstract

The international transition toward clean energy infrastructures has accelerated the global deployment of high-capacity industrial assets operating under highly volatile, multi-physical environments. Establishing a cohesive analytical linkage between upstream tier-specific manufacturing tolerances and downstream operational reliability remains a persistent and complex challenge, frequently compounded by historical telemetry data gaps and severe multi-variable non-linearity. This paper develops an integrated multi-echelon quality governance framework that structurally unifies mechanical failure mechanisms with advanced data-driven predictive algorithms. In contrast to conventional, idealized supply chain management paradigms, our methodology explicitly accounts for spatial-temporal data sparsity and regional logistics constraints, utilizing a hybrid physics-data driven architecture to predict degradation patterns and optimize buffer allocation. The empirical findings indicate that while the integration of real-time monitoring and advanced asset scheduling significantly mitigates unexpected field downtime, inherent structural biases within sensor placement loops can, to some extent, limit the instantaneous scalability of closed-loop predictive systems. Consequently, further cross-disciplinary research is needed to completely decouple systemic asset degradation from external environment-driven shocks, thereby elevating current quality control practices to a more robust theoretical altitude.